Eero Jäppinen. Acta Universitatis Lappeenrantaensis 527

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1 Eero Jäppinen THE EFFECTS OF LOCATION, FEEDSTOCK AVAILABILITY, AND SUPPLY-CHAIN LOGISTICS ON THE GREENHOUSE GAS EMISSIONS OF FOREST-BIOMASS ENERGY UTILIZATION IN FINLAND Thesis for the degree of Doctor of Science (Technology) to be presented with due permission for public examination and criticism in the Auditorium of Mikkeli University Consortium, Mikkeli, Finland on the 4 th of October, 2013, at noon. Acta Universitatis Lappeenrantaensis 527

2 Supervisor Professor Tapio Ranta Lappeenranta University of Technology Laboratory of Bioenergy Technology Mikkeli, Finland Reviewers D.Sc. Dimitris Athanassiadis Swedish University of Agricultural Sciences Department of Forest Biomaterials and Technology Umeå, Sweden D.Sc. Perttu Anttila Finnish Forest Research Institute Joensuu Research Unit Joensuu, Finland Opponent Professor Margareta Wihersaari Åbo Academy Sustainable Bioenergy Research Group Vaasa, Finland ISBN ISBN (PDF) ISSN-L ISSN Lappeenranta University of Technology Yliopistopaino 2013

3 ABSTRACT Eero Jäppinen The effects of location, feedstock availability, and supply-chain logistics on the greenhouse gas emissions of forest-biomass energy utilization in Finland Lappeenranta pages Acta Universitatis Lappeenrantaensis 527 Diss. Lappeenranta University of Technology ISBN ISBN (PDF) ISSN-L ISSN Forest biomass represents a geographically distributed feedstock, and geographical location affects the greenhouse gas (GHG) performance of a given forest-bioenergy system in several ways. For example, biomass availability, forest operations, transportation possibilities and the distances involved, biomass end-use possibilities, fossil reference systems, and forest carbon balances all depend to some extent on location. The overall objective of this thesis was to assess the GHG emissions derived from supply and energy-utilization chains of forest biomass in Finland, with a specific focus on the effect of location in relation to forest biomass s availability and the transportation possibilities. Biomass availability and transportation-network assessments were conducted through utilization of geographical information system methods, and the GHG emissions were assessed by means of lifecycle assessment. The thesis is based on four papers in which forest biomass supply on industrial scale was assessed. The feedstocks assessed in this thesis include harvesting residues, smalldiameter energy wood and stumps. The principal implication of the findings in this thesis is that in Finland, the location and availability of biomass in the proximity of a given energyutilization or energy-conversion plant is not a decisive factor in supply-chain GHG emissions or the possible GHG savings to be achieved with forest-biomass energy use. Therefore, for the 3

4 greatest GHG reductions with limited forest-biomass resources, energy utilization of forest biomass in Finland should be directed to the locations where most GHG savings are achieved through replacement of fossil fuels. Furthermore, one should prioritize the types of forest biomass with the lowest direct supply-chain GHG emissions (e.g., from transport and comminution) and the lowest indirect ones (in particular, soil carbon-stock losses), regardless of location. In this respect, the best combination is to use harvesting residues in combined heat and power production, replacing peat or coal. Keywords: Geographical Information System (GIS), Life Cycle Assessment (LCA), transportation, Greenhouse Gases (GHGs), logistics UDC 502/504:620.91: :

5 ACKNOWLEDGEMENTS The work for this thesis was carried out in the Bioenergy Research Group, LUT Energy, School of Technology, at Lappeenranta University of Technology. I want to thank the reviewers of this thesis, Dr. Dimitris Athanassiadis and Dr. Perttu Anttila, for their valuable comments. I also want to the supervisor of this thesis Professor Tapio Ranta for his valuable comments. Naturally, I also want to thank the co-authors of the attached papers, Mr. Olli-Jussi Korpinen, Dr. Juha Laitila and Professor Ranta. Thanks also go to Professor Risto Soukka for his comments. The biggest thanks go to Mr. O-J Korpinen, who helped me with numerous problems, especially, but not limited to, all kinds of problems related to computers or GIS. Mikkeli, September 2013 Eero Jäppinen 5

6 ABBREVIATIONS, UNITS, CONVERSION FACTORS AND PREFIXES WITH EXPONENT VALUES Abbreviations CH 4 methane CHP combined heat and power CO 2 carbon dioxide EC European Commission EU European Union EW small-diameter energy wood GHG greenhouse gas GIS Geographic information systems GWP global warming potential HR harvesting residues LCA Life-cycle assessment N 2 O nitrous oxide RED Renewable Energy Directive of the European Union ST stumps Units % percent h hours J joules m meters m 3 t W yr (yrs) cubic meters metric tons watts year (years) Conversion Factors 1 Wh = 3600 J 1m 3 solid = 2.5 m 3 loose (for comminuted forest biomass) 6

7 Prefixes and their exponent values k kilo, 10 3 M mega, 10 6 G giga, 10 9 T tera, P peta, E exa,

8 LIST OF ORIGINAL ARTICLES This thesis is based on the original papers listed below, which are referred to in the text by the Roman numerals given here. These papers are reprinted with the permission of the publisher. I Jäppinen E, Korpinen O-J, Ranta T (2011). Effects of Local Biomass Availability and Road Network Properties on the Greenhouse Gas Emissions of Biomass Supply Chain. ISRN Renewable Energy, Volume 2011, Article ID , 6 p. II Jäppinen E, Korpinen O-J, Ranta T (2013). The Effects of Local Biomass Availability and Possibilities for Truck and Train Transportation on the Greenhouse Gas Emissions of a Small- Diameter Energy Wood Supply Chain. Bioenergy Research, Volume 6: III Jäppinen E, Korpinen O-J, Ranta T (2013). GHG Emissions of Forest-Biomass Supply Chains to Commercial-Scale Liquid-Biofuel Production Plants in Finland (2013). GCB Bioenergy, published online 18 February IV Jäppinen E, Korpinen O-J, Laitila J, Ranta T (2013). Greenhouse Gas Emissions of Forest Bioenergy Supply and Utilization in Finland. Manuscript accepted for publication in Renewable & Sustainable Energy Reviews, 24 August The author is the main author of the work and fully responsible for the text, calculations, data analyses, and study settings of all papers and this doctoral thesis. Co-author Olli-Jussi Korpinen (papers I IV) provided expertise for the GIS assessments and created the software geoprocessing model used in the GIS assessments. Co-author Juha Laitila (Paper IV) provided data on forest operations. Co-author Tapio Ranta (papers I IV) commented on the manuscripts and was the supervisor of the thesis research. 8

9 TABLE OF CONTENTS ABSTRACT... 3 ACKNOWLEDGEMENTS... 5 ABBREVIATIONS, UNITS, CONVERSION FACTORS AND PREFIXES WITH EXPONENT VALUES... 6 LIST OF ORIGINAL ARTICLES... 8 TABLE OF CONTENTS INTRODUCTION Background Forest-biomass energy use and potential Current and future demand for forest biomass in Finland The motivation for the work Outline of the thesis Objective The papers included MATERIALS AND METHODS Data on forest-biomass potential Harvesting residues and stumps Energy wood Theoretical vs. practical forest-biomass availability GIS assessments Spatial allocation of forest-biomass potential Road network assessment Assessment of forwarding distances GHG assessments LCA LCA as a method Goal and scope Inventory analysis Impact assessment Interpretation EU RED methodology RESULTS Paper I Paper II Paper III Further work related to the results presented in papers I, II, and III Paper IV DISCUSSION

10 4.1 Implications The effect of location on total GHG emissions of forest-biomass supply Forest-biomass energy utilization and GHG savings The effect of feedstock selection on the GHG savings Assumptions and limitations related to GHG assessments and LCA as a method Biomass availability prospects The role of transportation Policy issues Finnish forest biomass vs. other biomasses Need for further research REFERENCES

11 1. INTRODUCTION 1.1 Background In 1997, as part of efforts to mitigate anthropogenic climate change, the Kyoto Protocol set binding greenhouse gas (GHG) reduction targets for 37 industrialized countries and the European Community (United Nations 1998). The collective GHG-emission reduction commitment of the European Union (EU) members was 8% by the years relative to their combined GHG-emission levels in 1990 (United Nations 1998). For Finland, as a member of the EU, the national emission reduction target was 0% relative to the emissions in 1990 (Official Journal of the European Communities 2002). Currently, it seems that both the EU-wide target levels and the national targets of Finland have been reached 1 (European Environment Agency 2012). After the first GHG-reduction commitment period, , the EU is further committed to reductions in GHG emissions between 2013 and 2020 such that the collective GHG emissions over this eight-year period are, on average, 20% lower than those in 1990 (European Union 2012). Furthermore, the EU is ready to commit to 30% reductions if other major economies reach a comprehensive international agreement on GHG-reduction efforts (European Commission 2010a). In 2009, as a key part of the EU s climate policies, the European Commission (EC) introduced the Renewable Energy Directive (EU RED) (European Commission 2009), which set binding national targets for renewable-energy use by 2020 for each EU member state. For Finland, the target is a 38% share of renewable energy in final consumption and a 10% share in transport, to which Finland is committed through the national Climate and Energy Strategy (Finnish Government 2013a). Finland has even set its own national target of 20% 2 for transportation (Ministry of Employment and the Economy 2011a, 2013a). With regard to international climate policies, energy produced with biomass-based fuels (i.e., bioenergy) is considered carbon-neutral and renewable (UNFCC 2006), and biomass-based energy is expected to play a key role on international, EU, and national level in climate- 1 Final data are not yet available for the full five-year period at the time of writing. 2 Taking into account double-counting of biofuels produced from waste of various types, residues, nonfood cellulosic material, and lignocellulosic material (European Commission 2009). 11

12 change mitigation (IEA 2012, European Commission 2011, Finnish Government 2013a). In Finnish renewable-energy policies, forest-based bioenergy has been identified as the most significant and cost-efficient way of increasing the proportion of renewable energy in Finland (Finnish Government 2013a). In addition to introducing targets for the share of renewable energy, the EU RED set forth binding criteria for GHG savings with biofuels and bioliquids 3, to ensure that the production and use of biomass-based fuels actually reduces GHG emissions. In 2010, the European Commission further recommended that the Member States introduce requirements in their national sustainability programs for solid and gaseous biomasses 4, as utilized in the powerplant sector, that are similar to those laid down in the EU RED for liquid biofuels (European Commission 2010b). These GHG savings requirements include 35% savings in comparison to EU-wide fossil-fuel comparator 5 values, and they will become stricter, reaching a requirement of 50% in 2017 and 60% savings in 2018 for new installations. The Finnish Government has also proposed that the EU s sustainability requirements be introduced in the national legislation of Finland (Finnish Government 2013b), with the associated national act on sustainability of biofuels and bioliquids due to come into force in July 2013 (Ministry of Employment and the Economy 2013). 1.2 Forest-biomass energy use and potential In 2010, global primary energy demand was approximately 530 EJ, with bioenergy accounting for approximately 10% of this. The figure includes biomass- and waste-based energy. Traditional noncommercial bioenergy, used mainly for cooking and heating in developing countries, accounted for 59% of total biomass energy use. The proportion of 3 Biofuels refers to liquid or gaseous fuel for transport that is produced from biomass, and bioliquids denotes biomassderived liquid fuel for energy purposes other than for transport, including electricity and heating and cooling (European Commission 2009). 4 Solid and gaseous biomasses refers to raw materials originating from agricultural crops and residues from forestry, the wood-processing industries, and organic waste (European Commission 2010). 5 Key fossil-fuel EU comparator values relevant for purposes of this thesis are as follows (European Commission 2009 and 2010). Electricity 198 gco 2 eq MJ -1. Heat produced with solid or gaseous biomasses 87 gco 2 eq MJ -1. Bioliquids used in heat production 77 gco 2 eq MJ -1. Biodiesel replacing fossil diesel in transport 83.8 gco 2 eq MJ

13 traditional bioenergy use is, however, expected to decline, while demand for bioenergy in other sectors is expected to more than double from the current use of 22 EJ to 50 EJ by 2035, largely driven by international and government renewable-energy policies (IEA 2012). Various terms, such as forest energy, forest biomass, forest chips, forest residues, woodfuel, fuelwood, energy wood, forest bioenergy, forest fuels, and modern fuel wood are used in both the literature and spoken language to describe the same type of biomass feedstock for energy production. In this thesis, the term forest biomass means byproducts of forest operations conducted on forest land remaining forest land 6 (IPCC 2006), with the main aim being to produce higher-value material i.e., saw or veneer logs and pulpwood. This represents the current situation in Finland, wherein direct or indirect land-use change is not associated with forest-biomass use and in which forest-biomass supply is integrated with either industrial roundwood procurement or forest-management practices. The categories of forest biomass assessed in this thesis include harvesting residues, stumps, and small-diameter energy wood (HR, ST, and EW, respectively). The types of forest biomass assessed in this thesis can also be categorized as primary forest residues 7 (Nabuurs et al. 2007). The global potential of forest residues generated from forest fellings in 2005 is approximately 5 EJ 8, with the most promising regions being the USA and Canada, Central and Northern Europe, Russia, East Asia, Brazil, and Chile (Anttila et al. 2009a). The use of forest residues is, however, significantly lower than the potential. It has been estimated that annually approximately 1 EJ of energy is generated from forest residues (Sims et al. 2007). This leads to a rough estimate that 0.2% of global primary energy demand is now met with forest residues. Realizable forest residue potential in the EU has been estimated at 0.9 EJ a year; this includes logging residues, stumps, and small-diameter energy wood (Verkerk et al. 2011). This estimate of realizable potential, by Verkerk et al. (2011), takes into account several technical, 6 Forest land remaining forest land means managed forests that have been under forest land for over 20 years, or for over a country specific transition period (IPCC 2006). Land-use change is no t associated with biomass procurement from forest land remaining forest land. 7 In (Nabuurs et al. 2007) three main categories of forest residues that may be used for energy purposes have been defined: primary residues (available from additional stemwood fellings or as residues (branches) from thinning salvage after natural disturbances or final fellings); secondary residues (available from processing forest products) and tertiary residues (available after end use). 8 Stumps and small-diameter energy wood were not included in this potential, since their contribution was assumed to be marginal at the global level (Anttila et al. 2009a). 13

14 environmental, and social constraints, which makes it more realistic than figures for potential based only on annual forest growth. Another EU-level estimate at this level (0.7 EJ, including felling residues and stumps) has been presented by Asikainen et al. (2008). The actual use of forest residues has been estimated as having accounted for 0.35 EJ in the EU-24 plus Norway in 2006 (Junginger et al. 2010). It should be noted that the global and EU-level estimates stated for forest-residue potentials and use ( forest biomass in this thesis) are only rough estimates and depend on various assumptions and the estimation methods used (Anttila et al. 2009a, Torén et al. 2011). In recent years, several estimates of the potential of forest biomass for energy purposes in Finland have been presented (Helynen et al. 2007, Laitila et al. 2008, Kärhä et al. 2010, Salminen et al. 2010). The estimates of potential, falling within the range EJ, depend on the methods used and on various parameters. This range represents the potential that could be utilized in view of various technical, environmental, and economic constraints. In 2012 the total use of forest biomass (harvesting residues, stumps, and small-diameter energy wood) for energy production in Finland was 0.06 EJ (Finnish Forest Research Institute 2013a). With respect to estimation of forest-biomass potential in general, it should be noted that all estimates involve various and case-dependent assumptions. Four categories of potential can be distinguished: 1) theoretical potential, a maximum value limited by factors such as the physical or biological barriers that, given the current state of science, cannot be altered; 2) technical potential, limited by the technology used and the natural circumstances; 3) economic potential, the technical potential that can be met at economically profitable levels; and 4) ecological potential, which takes into account ecological criteria, such as soil erosion and loss of biodiversity (EUBIA 2012). Most estimates of potential feature characteristics from two or more of these four classes and thus represent techno-ecological or techno-economic potential e.g., potential as presented by Kärhä et al. (2010) and Ranta et al. (2007). 14

15 1.3 Current and future demand for forest biomass in Finland In 2011, 87% of the total use of 50 PJ 9 of forest biomass (HR, EW, and ST) in Finland was by power and heating plants and 13% by small-sized dwellings, most of them farms. Most of the forest biomass in Finland is used in combined heat and power (CHP) plants, producing energy for communities or industry, or, in many cases, both (Laitila et al. 2010, Kärhä et al. 2010). The type of forest biomass used most in the power and heating sector was EW, with a 49% share, followed by HR and ST, with 36% and 15%, respectively. Since 2000, Finland s total use of EW, HR, and ST for energy has increased almost sevenfold (Finnish Forest Research Institute 2013a). In the National Forest Program, the target for forest-biomass energy use in 2015 has been set at PJ 10 (Ministry of Agriculture and Forestry 2011), whereas the target set by the Finnish Government is 90 PJ in the heat and power sector by 2020 (Ministry of Employment and the Economy 2010, Finnish Government 2013a). The most significant increase in forest-biomass energy use is expected to come from replacement of peat in multifuel boilers and from substitution for coal in coal-fired boilers (Finnish Government 2013b). In addition to the heat and power sector, the demand for forest biomass in Finland may grow because of production of so-called second-generation liquid biofuels (Heinimö et al. 2011, Ministry of Employment and the Economy 2011b). A commercial-scale liquid-biofuel production plant could consume approximately 14 PJ yr -1 of forest biomass (UPM-Kymmene Oyj 2011, Metsäliitto 2011, Stora Enso Oyj 2010). However, because only one such plant in Finland was awarded funding by the EC in 2012, with that plant currently expected to commence operation in late 2016 (European Commission 2012), it can be assumed that the growth in biomass demand over the next few years will come mainly from the heat and power sector. The future is also expected to see significant growth in internationally traded volumes of biomass (Bahadur Magar et al. 2011, Heinimö 2011), which may result in export of forest biomass from Finland and further increase in demand for forest biomass. 9 Large and rotten roundwood unsuitable as raw material for the forest industry is not included in this figure, because it is not covered by the assessments in this thesis. Its use by the power and heating sector represented 7% of total forest-biomass use in Finland in 2011 (Finnish Forest Research Institute 2012). 10 Converted from wood-use volumes on the following assumption: 1 million m 3 = 7.2 PJ. 15

16 1.4 The motivation for the work Given that forest biomass is going to play an increasingly important role in energy production on national, European, and global level, the issues of resource-use efficiency in relation to climate-change mitigation and GHG emissions derived from forest-biomass supply and utilization need to be addressed accordingly. Forest biomass from natural forests represents a geographically distributed feedstock, and geographical location affects the GHG performance of a given forest-bioenergy system in several ways. For example, biomass availability, forest operations, transportation possibilities and the distances involved, biomass end-use possibilities, fossil reference systems, and forest carbon balances all depend to some extent on location. Furthermore, if demand for forest biomass grows as expected, it will lead to growing imbalance between demand points and supply areas on international, national, and even regional level (Tahvanainen and Anttila 2011, Ranta et al. 2007, Korpinen et al. 2012, Ranta 2005, Anttila et al. 2013), which, in turn, will result in longer feedstock transportation distances and more GHG emissions. Location-specific information has been used in many studies examining forest-biomass availability, supply costs, and logistics in Finland and in the Nordic region, such as the work of Tahvanainen and Anttila (2011), Rørstad et al. (2010), Ranta (2005), Nord-Lasen and Talbot (2004), and Korpinen et al. (2013). Also, the GHG emissions related to forest-biomass supply and energy utilization in Nordic conditions have been addressed in many studies, such as those of Lindholm et al. (2010), Gustavsson et al. (2011), Repo et al. (2012), Valente et al. (2011), and Wihersaari (2005). However, the author of this thesis is not aware of previous published research addressing GHG emissions of forest-biomass supply and utilization in a similar location-specific manner in Finland. The main motivation behind this work was the need for information on how, and to what extent, the location of a demand point, local biomass availability, and transportation possibilities affect the GHG emissions derived from forest-biomass supply and energyutilization chains in Finland. A further question is how important these emissions are in relation to the GHG savings that may be possible with use of biomass energy. Another motive was desire to introduce a novel way of utilizing geographically specific data in GHG-emission assessment of bioenergy systems. 16

17 1.5 Outline of the thesis Objective The overall objective of this thesis was to assess the GHG emissions derived from supply and energy-utilization chains of forest biomass in Finland, with a specific focus on the effect of location in relation to forest biomass s availability and the transportation possibilities. The main research questions of this thesis are these: 1) To what extent does the location of the biomass end user affect the GHG emissions arising from the forest biomass supply chain in Finnish conditions, in terms of gco 2 eq MJ -1 of comminuted forest biomass delivered to the end user? 2) What is the effect of any location-dependent differences in GHG emissions of feedstock supply chain to end user locations in Finland on the possible GHG reductions achieved with forest-biomass-based energy production? The main hypothesis adopted for this thesis project is that there are location-dependent differences in GHG emissions but these differences are not decisive in terms of possible GHG reductions achieved with forest-biomass use. A sub-hypothesis is that the location-dependent differences in emissions generated through forest-biomass supply can be compensated for through logistical choices for example, utilization of railway transportation from distant supply areas. The thesis focuses on forest-biomass supply on the scale typical for CHP plants in Finland (in papers I and II) and the possible scale of supply of a commercial-scale liquid-biofuel production plant (in Paper III). The approach of Paper IV is not tied to the scale of supply, but the end-use possibilities assessed in Paper IV represent industrial scale. Therefore, the results stated in this thesis do not accurately represent supply and utilization situations for smallscale users of forest biomass, such as housing or small heating networks, because in smallerscale applications the supply areas are smaller and the machinery used in the supply chain and energy-production equipment may differ significantly from those on which the assessments in this thesis are based. 17

18 1.5.2 The papers included The thesis is based on four papers, briefly described thus: Paper I: The Effects of Local Biomass Availability and Road Network Properties on the Greenhouse Gas Emissions of Biomass Supply Chain Paper I deals with a rather limited forest-biomass supply of 360 TJ yr -1 (100 GWh yr -1 ) and focuses on the method of assessing the effects of local biomass availability along with the effects of road network properties. In Paper I, two case studies of comminuted EW supply to CHP plant locations are presented, one in northern Finland (Rovaniemi) and one in southern Finland (Mikkeli). Since the only transportation method assessed in this study is truck-based transportation, the focus is on the differences in the GHG emissions of transportation between the two case-study locations. The scale of supply represents a large CHP plant. Research question for Paper I: What are the effects of local biomass availability and road network properties on the GHG emissions of forest-biomass transportation to CHP plants in Northern and Southern Finland when the amount supplied is 360 TJ yr -1? Paper II: The Effects of Local Biomass Availability and Possibilities for Truck and Train Transportation on the Greenhouse Gas Emissions of a Small-Diameter Energy Wood Supply Chain Paper II continues on this subject and with the same two locations as Paper I but with a larger supply, of 720 TJ yr -1 (200 GWh yr -1 ). In Paper II, also assessed are the possibilities for using railway transportation from distant supply areas with two possible arrangements of comminution (in roadside storage or at railway loading locations). Similarly to Paper I, Paper II presents a supply scenario for comminuted EW to two CHP plant locations, Mikkeli and Rovaniemi. In addition to the emissions from transportation, the assessments include comminution and loading operations. The scale of supply represents a large CHP plant. Research questions for Paper II: What are the effects of local biomass availability and road network properties on the GHG emissions of forest-biomass supply to CHP plants in Northern and Southern Finland when the amount supplied is 720 TJ yr -1? Can these emissions be reduced through the use of railway transportation? If transportation by rail is utilized, should the feedstock be chipped at roadside storage locations or, instead, at railway loading locations? 18

19 Paper III: GHG Emissions of Forest-Biomass Supply Chains to Commercial-Scale Liquid- Biofuel Production Plants in Finland Paper III continues on the same topic as papers I and II but considers a greater supply, 7.2 PJ yr -1, and different case-study locations. The case studies assessed in Paper III represent supply situations for three possible commercial-scale liquid-biofuel plants on the Finnish coast: Porvoo and Rauma, in southern Finland, and Kemi, in northern Finland. Possibilities for train transportation are assessed with multiple logistics scenarios. The supply scale represents possible demand for this type of forest biomass from domestic sources for a commercial-scale liquid-biofuel production plant. Research questions for Paper III: What are the GHG emissions derived from the supply of 7.2 PJ yr -1 of biomass to three possible liquid-biofuel plant locations in Finland? How much can these emissions be reduced through railway transportation from distant supply areas? Paper IV: Greenhouse Gas Emissions of Forest Bioenergy Supply and Utilization in Finland Paper IV assesses the effects of geographical location on the GHG balance of forest-biomass supply and utilization in a wider context. While the first three papers focus on biomass availability and long-distance transportation by truck (addressed in all three papers) and train (papers II and III), Paper IV covers all parts of the supply chain of forest biomass from forest operations to the comminuted feedstock ready for use for energy purposes. Furthermore, in addition to the GHG emissions of the supply chain, Paper IV assesses various possibilities for utilization of the feedstock in Finland and possible GHG-emission reductions that may be achieved. Research questions for Paper IV: What are the GHG emissions of the most typical supply chains for HR, ST, and EW in Finland, with all parts of the supply chain taken into account? What GHG reductions, if any, might be achieved with various utilization systems? How do the GHG emissions differ between Northern and Southern Finland? 19

20 2. MATERIALS AND METHODS 2.1 Data on forest-biomass potential Harvesting residues and stumps The forest-biomass availability assessments in papers I, II, and III are based on municipalitylevel estimates of technical potential (398 municipalities) of HR, ST, and EW provided by the Finnish Forest Research Institute. The estimates used for HR and ST in this thesis are derived from annual forest fellings in The amounts of HR and ST potentially available depend on final fellings; therefore, the potential estimations embody an assumption that the demand for saw and veneer logs in Finland is going to stay at the same level as in The raw material taken into account in these potential estimates includes spruce and pine HR from final fellings (Pinus sylvestris and Picea abies) and spruce ST. The methodology and availability constraints used in the potential estimation for HR and ST are presented by Laitila et al. (2008) Energy wood EW is collected from advanced seedling stands and first thinnings either as a separate operation or integrated with procurement of industrial roundwood. so its availability is not tied to industrial roundwood use as directly as that of HR and ST is. The potential of EW is based on the 10th national forest inventory and multi-source national forest inventory conducted by the Finnish Forest Research Institute. The methodology and the constraints applied in the assessment of EW potential are described by Anttila et al. (2009b). Of the three types of forest biomass assessed in this thesis, EW has the greatest potential for growing use in the future (Laitila et al. 2008, Hynynen 2008). The total potentials for HR, ST, and EW in Finland are 48.6, 19.4, and 46.8 PJ yr -1, respectively, totaling PJ yr -1. In 2011, the annual use of the kinds of forest biomass included in this study (HR, ST, and EW) was 57 PJ (Finnish Forest Research Institute 2013a). Accordingly, the current use in Finland corresponds to 50% of the estimated technical potential at national level. 20

21 2.2 Theoretical vs. practical forest-biomass availability For a more realistic assessment of the GHG emissions of the forest-biomass supply chain, the technical potential should be adjusted to reflect the actual availability. One key factor affecting the availability of biomass is whether or not the forest-owners are willing to sell (or give away) forest biomass for energy purposes. However, from survey results and the data available on this matter, no precise figure can be derived for forest-owners willingness to sell various types of forest biomass for energy purposes that would be representative for the whole country. According to results of surveys conducted in Finland (with varying geographical coverage and questions asked), the percentage of forest-owners who are willing to sell or would consider selling HR, ST, and EW is within the range 18 74%, 45 80%, and 18 87%, respectively (Mynttinen et al. 2013, Rämö et al and 2009, Karjalainen 2001, Järvinen et al In addition to forest-owners attitudes to selling forest biomass for energy purposes, other factors that may limit the availability of forest biomass to any one user include lack of information about suitable harvestable stands and about opportunities to sell EW, prohibitive harvesting and transportation costs arising from remoteness of location, and lack of the subsidies that are often required for economically feasible harvesting and collection especially of EW (Laitila et al. 2008, Maidell et al. 2008, Kärhä et al. 2010, Karhunen et al. 2011, Rämö et al. 2001, Karjalainen 2001, Ministry of Agriculture and Forestry 2012). In view of all these possible factors limiting forest-biomass availability, the actual potential was estimated to be 50% of the technical potential in papers I, II, and III of this thesis, so the potential of each supply point was adjusted by a factor of 0.5. Given that in 2012 around 50% of the technical potential was used for energy purposes, the 50% limitation can be considered to reflect the current situation in Finland accurately enough for the purpose of assessment of the GHG emissions derived from forest biomass in the supply chains. However, it should be noted, that the potential estimations used in this work reflect the current situation and current raw material procurement practices. In practice this means that, if end users are willing to pay more for the raw material, the amount of potentially available raw material may rise. Also, trees that could also be suitable for pulpwood production may be directed to energy use in the future in increasing quantities. It should be noted, though, that local competition over the same resources does limit the availability of forest biomass to any one user more in some regions than others (Korpinen et 21

22 al. 2013, Anttila 2013), with various influencing factors, among them the paying capacity, location, and market share of the competing demand points (i.e., the entities buying the biomass). Possible effects of competition for forest-biomass resources in different regions or parts of the country have not been further evaluated in this thesis or the papers included in it. 2.3 GIS assessments Spatial allocation of forest-biomass potential If one is to take local biomass availability into account in the assessment of GHG emissions arising from transportation activities, the spatial distribution assumed for forest-biomass resources should reflect the actual conditions in the relevant location. Therefore, in this thesis project, the municipal-level forest-biomass potential was allocated within a more detail-level grid in the following manner. In papers I, II, and III, the forest-biomass potential (as described in sections 2.1 and 2.2) was spatially allocated to the geographical reference area, Finland (see Figure 1, pane a). For this purpose, Finland was divided into a geographical grid of 2 2 km for papers I and II, and into a grid of 4 4 km for Paper III (see Figure 1, pane b). The grid for Paper III was less dense than those for papers I and II because the supply areas covered were significantly larger and a 4 4 km grid was assessed as producing accurate enough results for the purpose of that particular paper. In general, it can be stated that the larger the amounts of biomass supplied and the supply areas, the less fine the geographical grid needs to be. Accordingly, the area of each grid square of land was 4 km² for papers I and II and 16 km² for Paper III. The total sizes of the supply areas ranged roughly from 3,900 to 4,500 km 2 for Paper I, from 7,700 to 9,000 km 2 for Paper II, and from 26,000 to 64,000 km 2 for Paper III. For each grid square, the share of productive forest land was calculated by means of raster analysis with GIS software. The forest area taken into account in this study was forest land in the growth categories of forestry land as defined by the Finnish Forest Research Institute (2011). By definition, forest land is land area where the average growth of forest is >1 m³ ha -1 yr -1. The method enables the exclusion of unsuitable land areas that do not produce biomass for this specific purpose from the calculations (see Figure 1, panes c and d). These unsuitable areas include, for example, areas covered with water, urban areas, roads, fields, and also forest areas with poorer growing conditions. The land-use categories were based on the 22

23 SLICES land-use data, with 10 m 10 m raster density (National Land Survey of Finland 2011). Also, the center points of the grid squares that did not fall within Finnish territory, along with those completely offshore, were deleted from the grid. The total municipality-level forest-biomass potential was then allocated to the grid-square center points within each municipality in accordance with the amount of productive forest-land area within each square (see Figure 1, pane e). Figure 1. Simplified diagram of the supply-point grid system. a, b: Finland was divided into a geographical grid; c, d: Unsuitable areas for forest-biomass supply were excluded; e: The forest biomass within each grid square was allocated to the grid-square center points; f: Center points further than 1 km from the nearest road were excluded; g: Roads were grouped into three classes in line with the maximum speed limits Road network assessment The first part of the transportation chain for forest biomass from roadside storage to the demand points is practically always truck transportation. In supply areas where the road network density is poorer, longer distances may have to be driven between a roadside storage location and a given demand point than in areas with a denser road network. In addition to road network density, road type affects the GHG emissions of truck transportation, since driving on smaller roads, such as gravel forest roads, consumes more fuel. In order for the 23

24 GHG emission calculations to take these factors into account, road network assessment was conducted as described below. For papers I, II, and III, the forest-biomass supply points (the center points in the geographical grid) were linked directly to the nearest road. Points further than 1 km from the nearest forest road were excluded from the calculations (see Figure 1, pane f). This was done because if a supply point is too far from the nearest road, the forest-transportation distance i.e., forwarding distance was assumed to be too long for collection of forest biomass to be economically feasible. For definition of the supply areas around the demand points and railway loading points, the shortest possible route to each point was calculated with GIS software. Then, from the nearest supply point, the supply areas around each demand point were expanded and supply points included in the area until the desired total amounts of biomass were reached. The route calculations were based on the Finnish national road and street database, Digiroad (Finnish Transport Agency 2010). To take the various road types and their effects on the GHG emissions of truck transportation into account, road segments were classified into three distinct road types (see Figure 1, pane g), as used in the life-cycle inventory dataset (Gabi Databases 2013) utilized in the GHG calculations (see Table 1 in Paper II). This classification was based on the maximum speed limit for each road segment as stated in Digiroad (Finnish Transport Agency 2010). According to data provided by VTT Technical Research Centre of Finland (2012), the fuel consumption on roads categorized as urban roads, interurban roads, and motorways in the emission calculations corresponds to fuel consumption on small rural roads, regional roads, and highways, which are typical road types traveled in the transportation of forest biomass from roadside storage to power plants in Finland. For papers I, II, and III of this thesis, if a biomass supply point was inside the supply area(s) around each demand point or loading location, all of its biomass was considered to be collected. This resulted in the total amount of biomass collected being slightly different from the desired total amounts, because supply points were included in the supply areas one by one in order of driving distance from the demand point. Therefore, the amounts supplied were adjusted to be exactly the desired amounts, by means of correcting factors. A value for kilometers per unit energy content was calculated for all three road types in each supply scenario, and the values were then multiplied to yield the distances that would be driven on each road type for supply of exactly the desired amount of biomass. 24

25 2.3.3 Assessment of forwarding distances For Paper IV, to estimate the GHG emissions of forwarding, the distance from harvesting sites to the roadside was assessed as follows. The classification as forest land was conducted as described in Subsection First, the geographical reference area (Finland) was divided into a grid with 2 km 2 km density, and the midpoints of these 2 km 2 km grid squares that were further than 1 km from the nearest road were not taken into account (in line with calculation methods use in previous papers). Second, the 2 km 2 km grid squares were divided into a more detailed grid with a density of 100 m 100 m (1 ha). Areas of restricted use (such as protected areas) were excluded (Finland s Environmental Administration 2013). Finally, direct distances from the center points of each 1 ha square of forest land to the nearest suitable roads were calculated. These direct distances were multiplied by a factor of 1.4 to yield an estimation of the actual distances covered by the forwarder (Viitala et al. 2004). Highways were excluded from the assessment, since they are not suitable for roadside operations (storage, comminution, and loading) in forest-biomass supply (Viitala et al. 2004). 2.4 GHG assessments LCA LCA as a method In this thesis, including the four papers, the GHG emissions of forest-biomass supply and utilization are assessed by means of life-cycle assessment (LCA) methods. The ISO standard defines LCA as a compilation and evaluation of the inputs, outputs, and potential environmental impacts of a product system throughout its life cycle (ISO 2006). Because this thesis, including its papers, focuses only on GHG emissions, the assessments can also be defined in carbon footprint terms (Weidema et al. 2008). LCA is the method chosen by the European Union for bioenergy sustainability assessments (European Commission 2009 and 2010b). LCA involves four main phases: 1) the goal and scope definition phase, 2) the inventory analysis phase, 3) the impact assessment phase, and 4) the interpretation phase (ISO 2006). The characteristics of each phase in relation to this thesis are described in the following sections of the chapter. 25

26 2.4.2 Goal and scope The overall goal of this thesis was to assess the GHG emissions stemming from the supply and energy-utilization chains of forest biomass in Finland, with specific focus on the effect of location in relation to forest-biomass availability and transportation possibilities. The scope of each paper, including system boundaries, is presented in more detail in the included papers (see Chapter 1 of Paper I, Figure 2 in Paper II, Figure 1 in Paper III, and Figure 2 in Paper IV). The scope of Paper I is limited to feedstock transportation, and that of both papers II and III is limited to feedstock transportation and comminution. Therefore, these three assessments can be classified as gate-to-gate assessments, because they focus on only one or a few steps in the process in the full life cycle of the forest-biomass energy-utilization chain (Jiménez-González et al. 2000). On the other hand, Paper IV represents more comprehensive LCA, or cradle-to-grave assessment, for it includes all relevant parts of the bioenergy production and utilization chain (European Commission Joint Research Centre 2013). In addition to assessing the GHG emissions derived from the bioenergy production and utilization chain, Paper IV takes the assessment a step further by evaluating the possible emission reductions that may be achieved with the selected bioenergy systems in Finland. In addition to division into, for example, gate-to-gate or cradle-to-grave studies, which is based on the scope and boundaries, LCA studies can be categorized as attributional or consequential studies, on the basis of their approach to a given system and its possible consequences. Attributional studies provide information on the emissions and impacts arising from a certain system or subsystem, while consequential studies also assess the indirect consequences of the functions performed by the (sub)system studied (Ekvall and Weidema 2004, Brander et al. 2009, Finnveden et al. 2009). In this thesis, papers I, II, and III represent attributional studies they concentrate on the GHG emissions of certain parts of the bioenergy chains whereas Paper IV represents a consequential study: it evaluates not only the emissions arising from a given bioenergy system itself but also the emissions that, because of certain fossil-energy systems being replaced by the bioenergy system, are not released (i.e., emission savings achieved when forest biomass is used instead of fossil fuels). A vital part of any LCA study is the definition of the functional unit. The functional unit is the reference unit of the system, and the performance of the system under study is judged in terms of it (ISO 2006). The functional unit should be chosen such that the results provide useful 26

27 information in view of the goal and scope of the study. In this thesis, the functional units for papers I, II, III were selected to represent the annual demand for biomass in each case: 360 TJ, 720 TJ, and 7.2 PJ of comminuted forest biomass delivered to a demand point, respectively. For Paper IV, the functional unit was 1 MJ of comminuted forest biomass produced and delivered to a demand point. Paper IV also included the energy-production phase, for which the results were presented in terms of 1 MJ final energy produced; therefore, the functional unit for the latter part of the assessment was 1 MJ of final energy produced (heat, electricity, or both, depending on the bioenergy system in question). It should be noted that, even if the results of different studies are given in the same units (for example, in terms of gco 2 eq MJ -1 ), the functional unit must be taken into account in the interpretation of the results, as discussed by, for example, Rebitzer et al. (2004). For example, the emissions per unit of comminuted forest biomass delivered to a demand point increase as the amounts supplied grow, because of longer transportation distances, but the results are still given in the same units Inventory analysis The inventory analysis phase is an inventory of the input and output data of the systems studied. In this thesis, special focus was placed on the assessment of geographically dependent emissions, for which the GIS assessment of biomass availability, transportation network, and forwarding distances (as described in Section 2.2) provided site- and locationspecific initial data. In the included papers, data from the literature and Life Cycle Inventory (LCI) datasets (GaBi databases 2011, 2012, and 2013) were used (see the references in each paper for details). The GaBi databases are the largest internally consistent databases currently on the market (PE-International 2013, JRC 2013a), with consistent documentation in line with the European Life Cycle Data System conformity rules (JRC 2013b). Data sources of the same quality were used across the various papers, thus enabling drawing of conclusions based on the four papers as a whole. The emissions were calculated with LCA software Impact assessment The potential environmental effects a given system causes are assessed in the impact assessment phase. First, the inputs and outputs of the system, such as the resources used or emissions, are classified into various impact categories on the basis of the potential 27

28 environmental effects they may produce. For example, gaseous emissions to the air may be classified among, for example, substances that cause global warming or substances that cause photochemical ozone formation. A single substance may be placed into one or more categories. After classification, the emissions are characterized, meaning that each substance is assigned a relative factor, in accordance with the potential of said substance to cause a certain environmental effect. For example, the gases that may cause global warming are characterized (i.e., weighed) for their estimated potential to cause global warming on a given time horizon with respect to, for example, CO 2. This thesis focuses on GHG emissions, meaning that the only potential environmental impact focused on here is anthropogenic climate change i.e., human-induced global warming. More precisely, only those emissions with potential to cause the actual environmental effect in question are assessed, not the effect itself (i.e., rising of the average global temperature) or the physical climatic responses that the emissions may cause (Forster et al. 2007). GHGs cause radiative forcing, by definition the change in net (down minus up) irradiance (solarplus longwave; in W m 2 ) at the tropopause after allowing for stratospheric temperatures to readjust to radiative equilibrium, but with surface and tropospheric temperatures and state held fixed at the unperturbed values (Ramaswamy et al. 2001). However, radiative forcing is not assessed in this study; only the three most important gaseous substances that cause it are: carbon dioxide, methane, and dinitrogen oxide (CO 2, CH 4, and N 2 O, respectively) (IPCC 2007). This is in line with international climate agreements and with the EU RED methodology, in which the climatechange mitigation activities are associated with the amount of GHGs emitted. For this thesis, emissions were assessed in terms of global warming potential (GWP) on a 100-year time horizon. The GWP value can be used for estimation of the potential future climate impact of individual gases in a relative sense (Ramaswamy et al. 2001), and it forms the basis of, for example, the Kyoto Protocol, the EU RED, and the US Renewable Fuel Standard for long-term emissions (Forster et al. 2007, EEA 2012, Yacobucci and Bramcort 2010). In papers I and II, the GWP factors relative to CO 2 for CH 4 and N 2 O were 25 and 298, and in papers III and IV 23 and 296, respectively. This difference in GWPs arises from the fact that in papers I and II the impact-assessment methodology of CML 2001 (Nov. 2009) was used (Guinée et al. 2001), which applies the same GWP factors recommended by the IPCC (Forster et al. 2007), whereas in papers III and IV the GWP factors specified in the EU RED were used (European Commission 2009). However, the difference in these studies final results that 28

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